code: def _features():
      return tfds.features.FeaturesDict(
          {
              "sample_id": np.int32,
              "ambiguous_question": tfds.features.Text(
                  doc="Disambiguated question from AmbigQA."
              ),
              "qa_pairs": tfds.features.Sequence(
                  tfds.features.FeaturesDict(
                      {
                          "question": tfds.features.Text(),
                          "short_answers": tfds.features.Sequence(
                              tfds.features.Text(),
                              doc="List of short answers from AmbigQA.",
                          ),
                          "context": tfds.features.Text(
                              doc="Additional context provided."
                          ),
                          "wikipage": tfds.features.Text(
                              doc=(
                                  "Title of the Wikipedia page the additional context was"
                                  " taken from."
                              )
                          ),
                      }
                  ),
                  doc="Q&A pairs from AmbigQA which are used for disambiguation.",
              ),
              "wikipages": tfds.features.Sequence(
                  tfds.features.FeaturesDict(
                      {
                          "title": tfds.features.Text(
                              doc="Title of the Wikipedia page."
                          ),
                          "url": tfds.features.Text(
                              doc="Link to the Wikipedia page."
                          ),
                      }
                  ),
                  doc="List of Wikipedia pages visited by AmbigQA annotators.",
              ),
              "annotations": tfds.features.Sequence(
                  tfds.features.FeaturesDict(
                      {
                          "long_answer": tfds.features.Text(
                              doc="Annotation."
                          ),
                          "knowledge": tfds.features.Sequence(
                              tfds.features.FeaturesDict(
                                  {
                                      "content": tfds.features.Text(
                                          doc="A passage from Wikipedia."
                                      ),
                                      "wikipage": tfds.features.Text(
                                          doc=(
                                              "Title of the Wikipedia page the passage was"
                                              " taken from."
                                          )
                                      ),
                                  }
                              ),
                              doc="List of additional knowledge pieces.",
                          ),
                      }
                  ),
                  doc=(
                      "Long-form answers to the ambiguous question constructed by ASQA"
                      " annotators."
                  ),
              ),
          }
      )
  
file_path: datasets/tensorflow_datasets/datasets/asqa/asqa_dataset_builder.py
imports: import json
  import numpy
  from tensorflow_datasets.core.utils.lazy_imports_utils import tensorflow
  import tensorflow_datasets.public_api
label_desc: Use image node_id 1 for calling a global function with example usage: _features() and returns: tfds